An Augmentation Algorithm for the Maximum Weighted Stable Set Problem
Computational Optimization and Applications
An Optimisation Algorithm for Maximum Independent Set with Applications in Map Labelling
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
A fast algorithm for the maximum clique problem
Discrete Applied Mathematics - Sixth Twente Workshop on Graphs and Combinatorial Optimization
An efficient branch-and-bound algorithm for finding a maximum clique
DMTCS'03 Proceedings of the 4th international conference on Discrete mathematics and theoretical computer science
Coding for a bit-shift channel with applications to inductively coupled channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Error correcting coding for a nonsymmetric ternary channel
IEEE Transactions on Information Theory
A Branch and Cut solver for the maximum stable set problem
Journal of Combinatorial Optimization
Safe lower bounds for graph coloring
IPCO'11 Proceedings of the 15th international conference on Integer programming and combinatoral optimization
Combinatorial algorithms for the maximum k-plex problem
Journal of Combinatorial Optimization
A simple and faster branch-and-bound algorithm for finding a maximum clique
WALCOM'10 Proceedings of the 4th international conference on Algorithms and Computation
A combinatorial column generation algorithm for the maximum stable set problem
Operations Research Letters
Solving hard set covering problems
Operations Research Letters
A branch-and-cut algorithm for the maximum cardinality stable set problem
Operations Research Letters
Computational Optimization and Applications
Hi-index | 0.06 |
We present a branch and bound algorithm for finding a maximum stable set in a graph. The algorithm uses properties of the stable set polytope to construct strong upper bounds. Specifically, it uses cliques, odd cycles, and a maximum matching on the remaining nodes. The cliques are generated via standard coloring heuristics, and the odd cycles are generated from blossoms found by a matching algorithm. We report computational experience on two classes of randomly generated graphs and on the DIMACS Challenge Benchmark graphs. These experiments indicate that the algorithm is quite effective, particularly for sparse graphs.